The present invention relates to the processing of time series data, and more specifically, to storage and retrieval of time series data in a database.
In applications involving the Internet of Things/sensors, it is required to process in real time large-scale time series data such as stock price fluctuations, temperature variations, blood pressure differences, tide tables, etc. Time series data consist of times and values.
Time series data contains timestamps and values associated with timestamps, e.g. containing sampling times and sampling values from a sensor. In various applications, such time series data need to be persistently stored in a database for query. Usually an approach to storing time series data in the prior art is to store the sampling times and the sampling values in one-to-one correspondence in a database. In association with such an approach, sampling times and sampling values are respectively used as keywords of an index file when creating the index file for the purpose of query. In such storage and indexed modes, data and index files occupy a large storage space, and the query speed is affected during data query because more I/O operations are needed. Waste of storage spaces during persistent storage of massive time series data and huge throughput requirements during querying massive time series data become especially prominent.
Therefore, there is a need to persistently store massive time series data with a low storage capacity while conveniently and rapidly querying such-stored massive time series data.